Factors Affecting Interprofessional Teamwork in Emergency Department Care of Polytrauma Patients: Results of an Exploratory Study
Bibliographic record
Abstract
Considering that traumatic injuries are the leading cause of death among young adults across the globe, emergency department care of polytrauma patients is a crucial aspect of optimized care and premature death prevention. Unfortunately, many studies have highlighted important gaps in collaboration among different trauma team professionals, posing a major quality-of-care challenge. Using the conceptual framework for interprofessional teamwork (IPT) of , the aim of this qualitative descriptive exploratory study was to better understand IPT from the perspective of health professionals in emergency department care of polytrauma patients, specifically by identifying factors that facilitate and impede IPT. Data were collected from a sample of 7 health professionals involved in the care of polytrauma patients through individual interviews and a focus group. In the second phase, 2 structured observations of polytrauma patient care were conducted. Following a thematic analysis, results revealed multiple factors affecting IPT, which can be divided into 5 broad categories: individual, relational, processual, organizational, and contextual. Individual factors, a category that is not part of the conceptual framework of , also emerged as playing a major part in IPT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".